Small businesses are told they need AI to stay competitive, yet 74% of companies that try it fail to derive any value, and 43% abandon their systems within 18 months. The problem isn't that the technology doesn't work; it's that businesses don't know if they're ready for AI in the first place. They either avoid it completely or go in unprepared and essentially waste money. I hope to build a machine learning tool specifically for micro-businesses that predicts whether they're ready to adopt AI. Micro-businesses face different challenges than larger small businesses, including fewer resources, less technical expertise, and higher failure rates with new technology. Previous research has studied AI adoption across all SMEs, but I want to see whether the factors that predict success differ for the smallest businesses. Using real data from over 11,000 European businesses, I'll train a model to identify what drives AI readiness in micro-businesses specifically. The final product will be a user interface where micro-business owners answer questions about their company and get a personalized readiness prediction with specific recommendations. I'll be interning at MITRE Corporation in cybersecurity during this project to develop my technical and analytical skills. This tool could help the most vulnerable businesses avoid mistakes when considering AI adoption.